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arxiv: 2605.06718 · v1 · submitted 2026-05-07 · 💻 cs.CR · cs.LG

TUANDROMD-X: Advanced Entropy and Visual Analytics Dataset for Enhanced Malware Detection and Classification

Parthajit Borah , Upasana Sarmah , D.K. Bhattacharyya , J.K. Kalita This is my paper

Pith reviewed 2026-05-11 00:54 UTC · model grok-4.3

classification 💻 cs.CR cs.LG
keywords malware datasetentropy analysisvisual featuresstatic malware analysismachine learning detectionmulticlass classificationcybersecurity
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The pith

TUANDROMD-X supplies entropy and visual features from static analysis to support better machine learning malware detectors.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper presents TUANDROMD-X as a new multiclass dataset containing malware and goodware samples. Each sample comes with entropy measures and visual representations derived from static analysis of the binaries. This resource aims to overcome the shortage of suitable datasets that hinders the creation of effective machine learning defenses against sophisticated malware. By relying on static methods, it avoids the time and resource costs of running samples in dynamic environments. The dataset enables quicker development of detection systems capable of identifying specific malware families.

Core claim

TUANDROMD-X is a dataset that provides visual and entropy-based features for each malware and goodware sample, obtained through static analysis, to distinctly identify malware from goodware and classify among malware types.

What carries the argument

The TUANDROMD-X dataset itself, which encodes entropy calculations and visual analytics as features for machine learning input.

If this is right

  • Models trained on these features can detect malware without needing to execute the samples.
  • Feature engineering effort is reduced since the dataset already includes the key entropy and visual attributes.
  • Researchers gain a benchmark for comparing classification performance across different malware families.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Such datasets could encourage more focus on lightweight, static-based detection in resource-constrained environments like mobile devices.
  • Future work might combine this with other analysis types to improve robustness against obfuscation techniques.

Load-bearing premise

Entropy values and visual patterns extracted from static binary examination suffice to tell malware apart from legitimate software and to group malware into families, with the samples reflecting today's threat landscape.

What would settle it

Training a classifier on TUANDROMD-X and then testing its accuracy on a fresh collection of malware samples from current real-world attacks that were not part of the dataset.

read the original abstract

Malware and malware-based attacks are becoming more prevalent and complex. Attackers regularly come up with new techniques that have the ability to evade conventional and signature-based malware defense. In order to address such threats, there is an increasing demand for advanced and better defense solutions. Machine learning-based techniques are efficiently capable of defending against malware and malware-based attacks. Nevertheless, creating and efficiently testing such techniques demand high-quality datasets having samples of various malware families as well as goodware. The lack of such datasets continues to be a major bottleneck in malware research. In this paper, we introduce TUANDROMD-X, a multiclass malware dataset with visual and entropy-based features of each sample, distinctly identifying malware from goodware. The dataset is created based on static analysis, lowering the overhead that comes with high feature engineering and dynamic analysis. As a result, TUANDROMD-X facilitates researchers and cyber-security experts to design faster and better malware detection systems.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The paper introduces TUANDROMD-X, a multiclass malware dataset containing visual and entropy-based features extracted from malware and goodware samples via static analysis. It positions the dataset as addressing the shortage of high-quality labeled data and enabling faster, more effective machine learning-based malware detection and classification systems.

Significance. A well-documented dataset with pre-computed static features could lower the barrier for ML experiments in malware research by avoiding repeated feature engineering or dynamic execution. The static-analysis approach is noted as reducing overhead relative to dynamic methods. However, the absence of any validation metrics, baseline classifier results, or comparisons to existing datasets means the claimed facilitation of 'better' detection systems remains an untested assertion rather than a demonstrated contribution.

major comments (2)
  1. [Abstract] Abstract: The central claim that TUANDROMD-X 'facilitates researchers and cyber-security experts to design faster and better malware detection systems' is unsupported by evidence. No classification accuracies, feature discriminability results, ablation studies on the entropy/visual features, or comparisons against prior datasets are supplied to show that the extracted features actually separate malware families from goodware or from each other.
  2. [Dataset Construction / Sample Collection] Dataset description sections: No metadata on sample collection dates, sources, family distribution, or temporal coverage is provided. Without these details it is impossible to evaluate whether the collected samples adequately represent current threats, which is a load-bearing assumption for the claim that the dataset enables improved detection of contemporary malware.
minor comments (1)
  1. [Feature Extraction] Clarify the precise definitions and computation methods for the entropy measures and visual representations (e.g., which entropy variant, image size or feature vector format) so that the dataset can be reproduced or extended by other researchers.

Simulated Author's Rebuttal

2 responses · 0 unresolved

Thank you for the opportunity to respond to the referee's report. We value the constructive criticism and address each major comment below, indicating planned revisions where appropriate. Our responses focus on clarifying the paper's scope as a dataset contribution while strengthening its documentation.

read point-by-point responses

  1. Referee: [Abstract] The central claim that TUANDROMD-X 'facilitates researchers and cyber-security experts to design faster and better malware detection systems' is unsupported by evidence. No classification accuracies, feature discriminability results, ablation studies on the entropy/visual features, or comparisons against prior datasets are supplied to show that the extracted features actually separate malware families from goodware or from each other.

    Authors: We agree that the abstract overstates the contribution by implying demonstrated improvements in detection performance without supporting experiments. The manuscript is a dataset paper whose primary goal is to release pre-computed static entropy and visual features to reduce repeated feature-engineering effort for the community. To correct this, we will revise the abstract to state that the dataset supplies ready-to-use features from static analysis, thereby enabling faster experimentation by researchers, while removing the unsubstantiated assertion that it produces 'better' detection systems. We will also add a brief note in the introduction clarifying the distinction between dataset release and empirical validation of downstream ML performance. revision: yes

  2. Referee: [Dataset Construction / Sample Collection] Dataset description sections: No metadata on sample collection dates, sources, family distribution, or temporal coverage is provided. Without these details it is impossible to evaluate whether the collected samples adequately represent current threats, which is a load-bearing assumption for the claim that the dataset enables improved detection of contemporary malware.

    Authors: We concur that detailed provenance metadata is essential for assessing the dataset's relevance to current threats. In the revised manuscript we will expand the dataset construction section with a table and accompanying text that reports sample sources, collection time window, the number of samples per malware family and goodware category, and any available temporal information. This addition will allow readers to evaluate representativeness directly. revision: yes

Circularity Check

0 steps flagged

Dataset description with no derivation chain or fitted predictions

full rationale

The paper is a dataset release describing TUANDROMD-X construction via static analysis and pre-computed entropy/visual features. It asserts that the dataset 'facilitates researchers... to design faster and better malware detection systems' but supplies no equations, models, predictions, or first-principles derivations. No parameters are fitted, no results are claimed from the dataset itself, and no self-citation chain supports any load-bearing step. The central claim is an untested assertion of utility, not a derived quantity that reduces to its inputs by construction. This matches the expected non-circular outcome for a pure dataset paper.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

This is a dataset release paper rather than a mathematical derivation, so the central claim rests on no free parameters, axioms, or invented entities.

pith-pipeline@v0.9.0 · 5480 in / 1056 out tokens · 31073 ms · 2026-05-11T00:54:34.319469+00:00 · methodology

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Reference graph

Works this paper leans on

18 extracted references · 18 canonical work pages

  1. [1]

    IEEE Security & Privacy9(5), 41–47 (2011)

    O’Kane, P., Sezer, S., McLaughlin, K.: Obfuscation: The hidden malware. IEEE Security & Privacy9(5), 41–47 (2011)

    work page 2011

  2. [2]

    In: 2010 International Conference on Broadband, Wireless Computing, Communication and Applications, pp

    You, I., Yim, K.: Malware obfuscation techniques: A brief survey. In: 2010 International Conference on Broadband, Wireless Computing, Communication and Applications, pp. 297–300 (2010). IEEE

    work page 2010

  3. [3]

    In: Twenty-third Annual Computer Security Applications Conference (ACSAC 2007), pp

    Moser, A., Kruegel, C., Kirda, E.: Limits of static analysis for mal- ware detection. In: Twenty-third Annual Computer Security Applications Conference (ACSAC 2007), pp. 421–430 (2007). IEEE

    work page 2007

  4. [4]

    ACM Computing Surveys (CSUR)52(5), 1–48 (2019)

    Or-Meir, O., Nissim, N., Elovici, Y., Rokach, L.: Dynamic malware analy- sis in the modern era—a state of the art survey. ACM Computing Surveys (CSUR)52(5), 1–48 (2019)

    work page 2019

  5. [5]

    ACM computing surveys (CSUR)44(2), 1–42 (2008)

    Egele, M., Scholte, T., Kirda, E., Kruegel, C.: A survey on automated dynamic malware-analysis techniques and tools. ACM computing surveys (CSUR)44(2), 1–42 (2008)

    work page 2008

  6. [6]

    Computer Science Review32, 1–23 (2019)

    Chakkaravarthy, S.S., Sangeetha, D., Vaidehi, V.: A survey on malware analysis and mitigation techniques. Computer Science Review32, 1–23 (2019)

    work page 2019

  7. [7]

    Freitas, S., Duggal, R., Chau, D.H.: A large-scale image database of malicious software

    work page

  8. [8]

    In: Proceedings of the 4th ACM Workshop on Security and Artificial Intelligence, pp

    Nataraj,L.,Yegneswaran,V.,Porras,P.,Zhang,J.:Acomparativeassess- ment of malware classification using binary texture analysis and dynamic analysis. In: Proceedings of the 4th ACM Workshop on Security and Artificial Intelligence, pp. 21–30 (2011)

    work page 2011

  9. [9]

    In: Proceedings of the 27th ACM SIGSOFT International Symposium on Software Testing and Analysis, pp

    Nguyen, V.T., Namin, A.S., Dang, T.: Malviz: an interactive visualization tool for tracing malware. In: Proceedings of the 27th ACM SIGSOFT International Symposium on Software Testing and Analysis, pp. 376–379 (2018)

    work page 2018

  10. [10]

    In: International Conference on Detection of Intrusions and Malware, and Vulnerability Assessment (DIMVA’17), pp

    Wei, F., Li, Y., Roy, S., Ou, X., Zhou, W.: Deep ground truth analysis of current android malware. In: International Conference on Detection of Intrusions and Malware, and Vulnerability Assessment (DIMVA’17), pp. 252–276. Springer, Bonn, Germany (2017) 20TUANDROMD-X: Advanced Entropy and Visual Analytics Dataset for Enhanced Malware Detection and Classification

    work page 2017

  11. [11]

    In: 2020 IEEE 4th Conference on Information & Communication Technology (CICT), pp

    Borah, P., Bhattacharyya, D., Kalita, J.: Malware dataset genera- tion and evaluation. In: 2020 IEEE 4th Conference on Information & Communication Technology (CICT), pp. 1–6 (2020). IEEE

    work page 2020

  12. [12]

    Springer, ??? (2011)

    Gray, R.M.: Entropy and Information Theory. Springer, ??? (2011)

    work page 2011

  13. [13]

    Computing in science & engineering9(03), 90–95 (2007)

    Hunter, J.D.: Matplotlib: A 2d graphics environment. Computing in science & engineering9(03), 90–95 (2007)

    work page 2007

  14. [14]

    In: The 2nd Canadian Conference on Computer and Robot Vision (CRV’05), pp

    Gallagher, A.C.: Detection of linear and cubic interpolation in jpeg com- pressed images. In: The 2nd Canadian Conference on Computer and Robot Vision (CRV’05), pp. 65–72 (2005). IEEE

    work page 2005

  15. [15]

    Pattern Recognition Letters118, 14–22 (2019)

    Yao, G., Lei, T., Zhong, J.: A review of convolutional-neural-network- based action recognition. Pattern Recognition Letters118, 14–22 (2019)

    work page 2019

  16. [16]

    Progress in Artificial Intelligence9(2), 85–112 (2020)

    Dhillon, A., Verma, G.K.: Convolutional neural network: a review of models, methodologies and applications to object detection. Progress in Artificial Intelligence9(2), 85–112 (2020)

    work page 2020

  17. [17]

    In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp

    Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., Torralba, A.: Learning deep features for discriminative localization. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2921–2929 (2016)

    work page 2016

  18. [18]

    In: 2017 IEEE Winter Conference on Applications of Computer Vision (WACV), pp

    Smith, L.N.: Cyclical learning rates for training neural networks. In: 2017 IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 464–472 (2017). IEEE

    work page 2017